Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment
20
Scopus Publications
Scopus Publications
Optimal reactive power dispatch using modified-ant lion optimizer with flexible AC transmission systems devices Sela Naga Venkata Sri Krishna Chaitanya, R. Ashok Bakkiyaraj, Bathina Venkateswara Rao, Kalikrishnan Jayanthi Bulletin of Electrical Engineering and Informatics, 2025 This study focuses on reactive power planning in the IEEE30-bus test system, specifically involving the integration of flexible AC transmission systems (FACTS) within the utility system. The primary objective is to minimize power loss and voltage deviation. To address this, a recently developed optimization algorithm called modified ant-lion optimizer (MALO) is applied to solve the optimal reactive power dispatch (ORPD) problem on the IEEE 30-bus system. A comparative analysis is conducted between the results obtained with and without FACTS devices. The findings reveal that the utilization of FACTS devices leads to significantly improved outcomes compared to scenarios without FACTS devices. Among the FACTS devices studied, the unified power flow controller (UPFC) demonstrates superior performance compared to the static synchronous compensator (STATCOM) and interline power flow controller (IPFC).
Seismic Activity Monitoring Using IoT Mutyala Sumanth, S. N. V. S. K. Chaithanya, Nagababu Nouluri, Jayanth Kokkiligadda, Purna Nanda Puthi Lecture Notes in Electrical Engineering, 2025
Simulation of a solar power generation system based on a boost converter using fuzzy logic controller-based mppt Rupanjani Venkata Kishore Bobbili, S.N.V.S.K Chaitanya, Prudhvi Narayana Botcha, Naga Venkatesh Dara, Sridevi Dasam, Sundar Samuel Miriyala Iop Conference Series Earth and Environmental Science, 2025 The most significant energy source is photovoltaic (PV) since it is clean, pollution free, and limitless. A technical term for turning solar photon energy into direct current (DC) electrical energy is photovoltaic (PV). The converter and controller for PV systems are now being developed to increase power extraction efficiency and reduce costs, even though PV power generation is still deficient. Once the MPPT algorithm identifies the maximum power point, it records the corresponding voltage and current values. These recorded values represent the optimal operating point for the solar panel under the current environmental conditions. The algorithm then adjusts the voltage-to-current ratio to maintain this optimal operating point and maximize power output. Due to changes in sun irradiation and weather, solar array output varies. The fuzzy logic controller-based MPPT is applied in the boost converter by simulating the PV array using the MPPT method in MATLAB/Simulink software to enable PV arrays to function at their maximum power point.
Smart Energy Monitoring: A Case Study on PLC Based Data Acquisition and Python Analytics S.N.V.S.K. Chaitanya, Pusalapati Yuva Chakri, Lanka Shanmukha Sai, Koppula Praveen Kumar, Penumaka Santhosh, P.S.D.S. Badarinath 2025 IEEE International Conference on Smart Power Energy Renewables and Transportation Spert 2025 Proceedings, 2025 Environmentally safe operation and energy efficiency are ever more important in educational institutions. This platform, which relies on PLCs and Python-based analysis tools, monitors energy usage in real-time in most departments, which uncover opportunities for future consumption and peak usage intervals. The objective is to provide a scalable platform that will be capable of recognizing trends and deviations in use of power Electrical measurement is performed by a data collection system from various campus locations and stored in a single, centralized database to be analyzed. This pre-emptive move can lead to greater operational efficiency, cost-effectiveness, and to guarantee sustainability. In our project, we compared the power utilization of the Civil and the EEE blocks. The power consumption was lower in the Civil block with traditional spaces classrooms but within the EEE block with technology-enriched classrooms. By seeing to it that some optimization methods, these results encourage sustainability initiatives and serve as a model to other academic institutions.
Fractional order modeling based optimal multistage constant current charging strategy for lithium iron phosphate batteries K. Dhananjay Rao, Anilkumar Chappa, SVNSK Chaitanya, A. Hemachander, B. Phani Teja, Subhojit Dawn, Miska Prasad, Taha Selim Ustun Energy Storage, 2024 The primary power source for electric vehicles (EVs) is batteries. Due to the superior characteristics like higher energy density, power density, and life cycle of the lithium iron phosphate (LFP) battery is most frequently chosen among the various types of lithium‐ion batteries (LIBs). The main issues that users encounter are the time required to charge an EV battery and the safety of the EV battery during the charging period. The fast‐charging means, charging a battery with high currents which may lead to a rise in the temperature of a battery. The abrupt rise in battery temperature may cause changes in the internal chemical structures of the battery, reducing battery life even further. In this regard, an optimal charging profile design is of utmost importance in order to satisfy dual objectives simultaneously such as less charging time and improvement in life of the battery. To overcome the conflict between charging speed and rise in temperature an optimal multistage constant current (MSCC) based charging strategy has been investigated under different operating conditions. In addition, the proposed charging profiles have been studied using experimentation.
Smart Trolley for Supermarkets Using IoT Priyanka Veeranala, S.N.V.S.K Chaitanya, Narayana Eedara, Jayanth Pedakapu 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies Iccigst 2024 Proceedings, 2024 In the ever-evolving retail and logistics industry, there is a growing demand for innovative solutions that enhance operational efficiency and elevate the overall shopping experience. Traditional shopping trolleys often lack advanced features that can cater to the changing needs of modern consumers and retailers. Consequently, there is a compelling need for a smart trolley system that integrates cutting-edge technologies to revolutionize shopping. The smart trolley integrates components seamlessly to create an efficient and user-friendly shopping experience. The RFID reader enables smooth product identification and tracking, allowing customers to add items to their trolleys effortlessly. The IoT device facilitates real-time communication, ensuring instant data exchange with the central system for accurate inventory management and remote-control capabilities. The database stores and manages product details, optimizing inventory and enabling personalized shopping experiences. The keypad allows customers to input additional information, enhancing customization. The load cell accurately measures the weight of items, providing real-time feedback. The Arduino Mega acts as the central control unit, coordinating the functionalities of the components, ensuring smooth communication, and creating a reliable and efficient shopping experience for customers.
Machine Learning for Intelligent EV Charging Slot Booking During Low-Cost Grid Periods Narayana Eedara, N. V. S. K Chaitanya Sela, Priyanka Veeranala, Jayanth Pedakapu 2024 IEEE 9th International Conference for Convergence in Technology I2ct 2024, 2024 Electric vehicles (EVs) are becoming increasingly popular worldwide due to their acknowledged environmental advantages, which coincide with global efforts to mitigate climate change. The popularity of pollution-free, affordable maintenance, government incentives, and technological developments in charging, drive customers to increase the use of electric vehicles (EVs). On distribution systems, however, the spike in power demands brought on by EV charging presents problems like excessive voltage swings, power outages, and possible overloads. It becomes necessary to have a strong power system management system that can protect the distribution grid from negative impacts by controlling EV charging intelligently to lessen these problems.This necessity led to the development of an inventive EV charging management system that optimizes EV routing to charging stations by utilizing machine learning (ML) techniques. The main objectives are minimizing load variance, minimizing power losses, stabilizing voltage swings, and cutting charging costs. This ML-driven strategy makes strategic management of EV charging following minimum grid loads possible, based on analyzing previous data to create accurate predictions about future grid circumstances. Additionally, the system incorporates an easy-to-use website that offers EV owners a handy interface to reserve charging places based on anticipated minimum loads. By providing real-time information, predictive slot booking features, customizable preferences, and seamless payment integration, this website improves user experience. It offers a comprehensive solution that prioritizes user convenience in the ever-evolving field of sustainable mobility, while also addressing grid management challenges and reducing the charging cost.
Solar Water Pumping System with BLDC Motor and ANN-based MPPT Technique Mutyala Sumanth, S. N. V. S. K Chaithanya, Naga Babu Nouluri, Jayanth Kokkiligadda, Purna Nanda Puthi 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies Iccigst 2024 Proceedings, 2024 This paper outlines the development and simulation of a solar water pumping setup that incorporates a brushless DC (BLDC) motor and utilizes an Artificial Neural Network (ANN) for maximum power point tracking (MPPT). The system aims to provide an efficient and reliable solution for water pumping applications using renewable solar energy. A boost converter, bidirectional converter, and 3-phase inverter are modelled along with the BLDC motor, battery, and solar photovoltaic (PV) array. An ANN-based MPPT algorithm is implemented to maximize energy harvesting from the solar panels. The system performance is analyzed under varying solar irradiation conditions. The outcomes of the simulation affirm the efficacy of the suggested method in achieving maximum power extraction, maintaining a consistent voltage supply to the BLDC motor, and ensuring continuous operation even in the face of variations in solar energy output. The system exhibits a smooth transition between solar and battery power, validating the hybrid architecture. This research provides a foundation for implementing intelligent control of solar water pumping systems to enhance efficiency and reliability.
